One way to apply deep learning based methods such as CNN on 3D points is to transform these onto volumetric grid [1]}, [2]}, [3]}, [4]}, [5]}.
Further, 3D point clouds can also be projected onto several image planes and thus mapped into a 2-dimensional representation. The final output is generated by applying 2D CNN on the representations, and feature fusion [6]}, [7]}. However, these methods were inefficient in creating dense grids on projection planes due to the sparse nature of point clouds. These planes can significantly impact recognition ability, and occlusion in 3D can affect the accuracy.
